机译:基于最佳基的小波包熵特征提取和分级EEG分类用于癫痫检测
Department of Computer Science and Technology, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China,The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China;
Department of Computer Science and Technology, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China,The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China;
Department of Computer Science and Technology, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China,The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China;
electroencephalogram (eeg); feature extraction; wavelet packet entropy; epileptic detection; hierarchical knowledge base;
机译:基于小波的特征提取癫痫癫痫发作脑电图分类
机译:基于熵的特征提取技术与小波包变换相结合的多思想任务分类
机译:使用图像处理算法从脑图中提取特征,以检测癫痫发作活动的脑电信号分类
机译:使用新型熵特征的癫痫发作分类应用于脑电信号的最大重叠离散小波包变换
机译:字典投影追踪:一种用于声谱特征提取的小波包技术。
机译:利用小波包变换中基于Rényi最小熵的特征选择进行多类EEG信号分类
机译:利用基于Rényi的Min-entopy的特征选择,从小波包变换中选择多磅EEG信号分类